Wrist fractures are highly prevalent among children and can significantly impact their daily activities, such as attending school, participating in sports, and performing basic self-care tasks. If not treated properly, these fractures can result in chronic pain, reduced wrist functionality, and other long-term complications. Recently, advancements in object detection have shown promise in enhancing fracture detection, with systems achieving accuracy comparable to, or even surpassing, that of human radiologists. The YOLO series, in particular, has demonstrated notable success in this domain. This study is the first to provide a thorough evaluation of various YOLOv10 variants to assess their performance in detecting pediatric wrist fractures using the GRAZPEDWRI-DX dataset. It investigates how changes in model complexity, scaling the architecture, and implementing a dual-label assignment strategy can enhance detection performance. Experimental results indicate that our trained model achieved mean average precision (mAP@50-95) of 51.9\% surpassing the current YOLOv9 benchmark of 43.3\% on this dataset. This represents an improvement of 8.6\%. The implementation code is publicly available at https://github.com/ammarlodhi255/YOLOv10-Fracture-Detection

翻译：腕部骨折在儿童中极为常见，并可能严重影响其日常活动，如上学、参与体育运动以及完成基本自理任务。若处理不当，这些骨折可能导致慢性疼痛、腕部功能减退以及其他长期并发症。近年来，目标检测技术的进步在提升骨折检测方面展现出潜力，相关系统已达到甚至超越人类放射科医生的诊断准确率。特别是YOLO系列模型在该领域取得了显著成功。本研究首次对多种YOLOv10变体进行了全面评估，以考察其在GRAZPEDWRI-DX数据集上检测儿童腕部骨折的性能。研究探讨了模型复杂度调整、架构缩放以及实施双标签分配策略如何提升检测性能。实验结果表明，我们训练的模型在该数据集上取得了51.9\%的平均精度均值（mAP@50-95），超越了当前YOLOv9基准模型的43.3\%，实现了8.6\%的性能提升。实现代码已公开于https://github.com/ammarlodhi255/YOLOv10-Fracture-Detection。